dc.contributor.author |
Speidel, Ulrich |
en |
dc.coverage.spatial |
Singapore |
en |
dc.date.accessioned |
2018-10-01T23:48:47Z |
en |
dc.date.issued |
2015-12-02 |
en |
dc.identifier.uri |
http://hdl.handle.net/2292/38074 |
en |
dc.description.abstract |
Shannon observed that the normal distribution has maximal entropy among distributions with a density function and a given variance. This sparked a significant body of research in statistics, broadly concerned with goodness-of-fit estimators based on Shannon entropy for a variety of distributions and, in particular, normality testing. The present paper proposes to use compression algorithms and other parsing-based entropy estimators to match samples in sampling order to one of a set of distributions with the observed and, where applicable, , using the distributions’ quantile functions to convert the samples into a string of symbols for entropy estimation. The paper demonstrates with a series of Monte-Carlo simulations that the proposed technique may be able to distinguish between a number of common distributions even if the samples themselves are not i.i.d. |
en |
dc.relation.ispartof |
10th International Conference on Information, Communications and Signal Processing |
en |
dc.rights |
Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher. |
en |
dc.rights.uri |
https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm |
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dc.title |
Classifying Distributions via Symbolic Entropy Estimation |
en |
dc.type |
Conference Item |
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dc.rights.holder |
Copyright: The author |
en |
pubs.author-url |
http://www.icics.org/2015/program/programSessionSchedule.asp?SessionID=We33 |
en |
pubs.finish-date |
2015-12-04 |
en |
pubs.start-date |
2015-12-02 |
en |
dc.rights.accessrights |
http://purl.org/eprint/accessRights/RestrictedAccess |
en |
pubs.subtype |
Proceedings |
en |
pubs.elements-id |
504642 |
en |
pubs.org-id |
Science |
en |
pubs.org-id |
School of Computer Science |
en |
pubs.record-created-at-source-date |
2015-11-12 |
en |